Dynamic time warping (DTW) [1]}, [2]} uses a dynamic programming to align multiple time-series. DTW measures the similarity and finds an optimal match by inserting frames. Anguera et al. [3]} use a predefined similarity between graphemes and phonemes. Tapaswi et al. [4]} introduce a similarity between visual scenes and sentences based on appearance of same characters to align TV shows and plot synopses. Other dynamic programming approaches have also been used for multi modal alignment of text to speech [5]} and video [6]}. DTW was extended using canonical correlation analysis (CCA) for learning a mapping [7]}, [8]}, [9]}. CCA based DTW models aren't able to model nonlinear relationships. This has been addressed by Deep Canonical Time Warping (DCTW)  [10]}, which learns non-linear, correlated and temporally aligned representations of multiple time-series.
